How to Scrape AllEvents.in, EventsEye.com, District.in, and Meetup.com in a Single Automated Run

Introduction

Event data is fragmented. AllEvents.in covers general city events globally. EventsEye.com is the largest B2B trade show directory with 140+ countries. District.in handles ticketed entertainment. Meetup.com surfaces community and professional gatherings. Each platform has different content, different audience, and different data structure.

For developers, analysts, and automation teams that need comprehensive event intelligence, this fragmentation creates two compounding problems: you need to build and maintain four separate scrapers with different logic, and then normalise four different output schemas into one usable dataset.

This guide explains how the Universal Event Scraper by Techforce Global solves both problems with a single Apify Actor and how to integrate it into n8n for a fully automated event intelligence workflow.

Why You Need Data From All Four Platforms

No single event platform is comprehensive enough for serious event intelligence work. Each platform has a distinct audience and content type that the others do not cover.

AllEvents.in
AllEvents.in is one of the most comprehensive general event directories available globally. It covers conferences, workshops, concerts, festivals, and expos across thousands of cities with particularly strong coverage of events in India and Southeast Asia. If you need city-level event data at any reasonable scale, AllEvents.in is the most complete single source.

EventsEye.com
EventsEye.com is a dedicated B2B trade show and exhibition directory covering 140+ countries, organised by industry, region, country, and city. It is the only platform in this set that includes organiser contact details making it essential for B2B outreach to trade show organisers. No general-purpose event scraper includes EventsEye data. This is a meaningful competitive gap that the Universal Event Scraper fills.

District.in
District.in focuses on ticketed entertainment and live events concerts, comedy shows, cultural performances. Its ticket price data is more consistently structured than AllEvents, making it the most reliable source for price-related analysis.

Meetup.com
Meetup.com surfaces community and professional meetups tech groups, startup communities, hobby gatherings, and networking events that do not appear on any of the other three platforms. For teams targeting developer communities, startup founders, or professional groups, Meetup data is often the most relevant.

The Technical Problem: Four Scrapers, Four Schemas

Building individual scrapers for these platforms is not just a one-time cost it is an ongoing maintenance burden. Each platform requires:

Custom scraping logic for its specific HTML structure and dynamic content loading patterns
Custom pagination handling each platform paginates differently and at different scales
Custom date and price normalisation formats vary significantly across platforms
Custom field mapping 'event_title', 'name', 'title', 'eventName' are all used across the four
Separate error handling, retry logic, and proxy management for each source
Ongoing maintenance whenever any platform updates its layout or structure

Even when all four scrapers work correctly, the output is four separate datasets that cannot be merged without an additional ETL step. That ETL step must be rebuilt every time a source platform changes its output structure.

The Solution: Universal Event Scraper on Apify

The Universal Event Scraper handles all four platforms in a single actor run with a unified output schema. No separate scrapers to maintain. No ETL step to build.

Input Configuration
The actor accepts three inputs platform selection, location, and result limit:

Input example:
{ source: ['allevents', 'eventseye', 'district', 'meetup'], location: 'Chicago', limit: 50 }

source — array: select any combination of the four platforms
location — string: city name used across all scrapers
limit — number: maximum events per source (default 50, max 100 per source)

With limit: 50 and all four sources → up to 200 total records in one run

Output Schema 8 Consistent Fields
Every record from every platform returns the same eight fields:

Sample output record:
{ source: 'eventseye', title: 'Tech Expo Chicago 2026', url: 'https://eventseye.com/...', date: 'Mon, 15 Jun 2026', city: 'Chicago', venue: 'McCormick Place', price: 'N/A', venue_address: '2301 S King Dr, Chicago, IL 60616' }

source — which platform (allevents / eventseye / district / meetup)
Fields unavailable for a given source → returned as 'N/A' not null
Same 8 fields every time → no conditional handling needed in your pipeline

Practical Use Cases With Example Inputs

Use Case 1 - B2B Sales: Weekly Event Intelligence for Outreach
Goal: Every Monday, automatically generate a list of upcoming trade shows, conferences, and meetups in your target city for the sales team's outreach calendar

source: ['allevents', 'eventseye', 'meetup']
location: 'London'
limit: 100

Run schedule: every Monday 8:00 AM
Output: up to 300 events across 3 sources filtered by your team for outreach

Use Case 2 - Event Directory Builder: City Guide Data Feed
Goal: Maintain a comprehensive event directory for a city guide app all event types, updated weekly.

source: ['allevents', 'district', 'meetup']
location: 'Bangalore'
limit: 50

Run schedule: every Friday 6:00 PM
Output: up to 150 events → direct database insert, no transformation needed

Use Case 3 - Trade Show Organiser Outreach (EventsEye Only)
Goal: Pull trade shows in a target industry with organiser contact details for a B2B partnership outreach campaign.

source: ['eventseye']
location: 'Frankfurt'
limit: 100

Output: up to 100 exhibitions with organiser name, contact details, event website

n8n Workflow Integration Fully Automated Event Intelligence Pipeline

The actor connects to n8n via the Apify HTTP API. Here is a complete weekly automated pipeline:

Schedule Trigger node fires every Monday at 8:00 AM IST
HTTP Request node POST to Apify API to start the Universal Event Scraper actor run
Wait node polls every 30 seconds until run status = SUCCEEDED
HTTP Request node GET request to Apify dataset API to fetch all records
Google Sheets node appends all new records to your master event tracking sheet
Filter node filters source = 'eventseye' records for trade shows with contacts
HubSpot / CRM node creates lead records from EventsEye organiser contacts